Assistant Professor - Biomedical AI, Spatial Multi-Omics and Cancer Data Science, Department of Cellular, Molecular and Genetic Medicine

The Department of Cellular, Molecular and Genetic Medicine at Virginia Commonwealth University School of Medicine is committed to advancing molecular discovery, disease biology, and translational medicine through innovative research, education, and collaboration. The Division of Biomedical AI within CMGM is being developed to integrate artificial intelligence, machine learning, computational methods, and biomedical data infrastructure with molecular discovery and cancer research.

This position will support highly collaborative research across CMGM, Massey Comprehensive Cancer Center, the School of Medicine, and other VCU research programs. The goal is to strengthen collaborative cancer research through advanced spatial multi-omics analysis, scalable data systems, reproducible computational pipelines, AI-enabled research workflows, and cloud-based biomedical data infrastructure.

The Department of Cellular, Molecular and Genetic Medicine seeks a full-time, 12-month, non-tenure-track Research Assistant Professor in Biomedical AI, Spatial Multi-Omics, and Cancer Data Science. This is a 100% research position focused on collaborative and team-based biomedical research, with particular emphasis on supporting cancer center projects and translational research programs.

The candidate will bring expertise in spatial multi-omics, database management, data curation and provenance, reproducible pipeline development, AI agent development, and cloud computing. The candidate will work closely with investigators across CMGM, Massey Comprehensive Cancer Center, and the broader School of Medicine to develop, implement, and maintain computational infrastructure for high-impact biomedical and cancer research.

Contribute to collaborative biomedical and cancer research projects through the development and application of computational methods, data infrastructure, and AI-enabled research workflows.

Responsibilities include developing and maintaining reproducible pipelines for spatial transcriptomics, spatial proteomics, single-cell multi-omics, imaging, and related biomedical data types; designing data management systems that support data curation, metadata tracking, provenance, quality control, and reproducibility; supporting database development and integration for large-scale cancer and molecular medicine datasets; and implementing cloud-based computing workflows for scalable analysis and collaboration.

The candidate will also contribute to AI-enabled research infrastructure, including AI agents for data analysis, workflow automation, documentation, quality control, and reproducible biomedical discovery. The successful candidate will collaborate with faculty, clinicians, trainees, and research staff; contribute to manuscripts, grant applications, presentations, and reports; and help ensure that computational workflows are rigorous, transparent, scalable, and reusable across collaborative research programs.

Minimum Qualifications

• PhD or equivalent degree in bioinformatics, computational biology, biomedical data science, computer science, biostatistics, statistics, engineering, genomics, cancer biology, or a related field
• Demonstrated expertise in computational analysis of biomedical data, with experience in one or more areas such as spatial omics, single-cell analysis, multi-omics integration, cancer data science, biomedical AI, or translational bioinformatics
• Experience developing reproducible computational pipelines using programming languages such as Python, R, Nextflow, Snakemake, or related tools
• Experience with database management, data organization, metadata standards, data curation, quality control, and/or data provenance
• Demonstrated ability to work effectively in highly collaborative, multidisciplinary biomedical research teams
• Strong communication skills and ability to work with basic scientists, clinicians, computational scientists, trainees, and research staff
• Demonstrated ability to work in and foster an environment of respect, professionalism, and civility with a population of faculty, staff, and students from all backgrounds and experiences, or a commitment to do so as a faculty member at VCU

Preferred Qualifications

The candidate will have expertise in one or more of the following areas:

• Spatial transcriptomics, spatial proteomics, imaging-based spatial biology, single-cell genomics, or multimodal spatial multi-omics
• Cancer data science, tumor microenvironment analysis, cancer ecosystem modeling, immune-tumor interaction analysis, therapeutic response studies, or translational cancer research
• Development of scalable and reproducible pipelines for high-dimensional biomedical data analysis
• Database design, biomedical data models, metadata frameworks, data provenance, data harmonization, and long-term data stewardship
• Cloud computing and high-performance computing environments, including platforms such as Google Cloud, AWS, Azure, or institutional HPC systems
• AI agent development, workflow automation, LLM-enabled research tools, automated data analysis systems, or AI-assisted documentation and quality control
• Software engineering practices, version control, containerization, workflow management, API development, and collaborative code development
• Experience contributing to collaborative grant applications, manuscripts, cancer center research programs, shared resources, or team science initiatives

Applicants should submit a curriculum vitae, a brief research and technical expertise statement, a collaborative research statement, and names of references. Review of applications will begin immediately and continue until the position is filled.

A competitive startup and research support package will be provided to help the successful candidate develop collaborative biomedical AI, spatial multi-omics, and cancer data science infrastructure.